CPIDM: A Clustering-Based Profound Iterating Deep Learning Model for HSI Segmentation

Table 1

Number of training in addition to the test samples employed for the University of Pavia dataset.

Class name	Number	Training	Testing	Watershed transform [22]	NN cantered neuro-fuzzy approach [13]	Proposed CPIDM
Asphalt	6631	2210	4421	77.70	86.46	89.26
Meadows	18649	6216	12433	75.30	90.17	91.49
Gravel	2099	699	1400	77.27	85.04	88.37
Trees	3064	1021	2043	92.46	96.64	96.24
Painted metal sheets	1345	448	897	99.63	99.78	99.81
Bare soil	5029	1676	3353	79.50	92.39	94.89
Bitumen	1330	443	887	92.86	94.95	95.94
Self-blocking bricks	3682	1227	2455	76.45	85.36	90.44
Shadows	947	14291	28521	99.62	99.65	99.89